CSC412S Spring 2003 - Info


Course info sheets (ps)(pdf)

Instructor: Sam Roweis; email
Tutor: Ruslan Salakhutdinov; email

Please do NOT send Roweis or tutor email about the class directly to their personal accounts.
They are not able to answer class email except to

Lecture Times: Mondays, Wednesdays 10:10am -- 11:00 am
Lecture Location:Pratt 266
First lecture Jan6, last lecture April 9.
No lectures Feb 17/19 (Reading Week).

Tutorial Times: Fridays, 10:10am-11:00am
Tutorial Location: Pratt 266
First tutorial Jan 10, last tutorial April 11.
No tutorial Feb 21 (Reading Week).

Office Hours: Wednesdays 11-12 or by appointment

Prerequisite: CSC384H, 411H; CGPA 3.0; but permission of instructor can waive these
Load: 26L, 13T

Michael Jordan, An Introduction to Probabilistic Graphical Models
This textbook is not yet published, but drafts will be provided in class.

Marking Scheme
2 small assignments worth 10% each
2 larger assignments worth 15% each
1 midterm test worth 25%
1 final test worth 25%

Course Description

A senior undergraduate class on graphical models and probabilistic networks in AI.

Representing uncertain knowledge using probability and other formalisms. Qualitative and quantitative specification of probability distributions using graphical models. Algorithms for inference with graphical models. Statistical approaches and algorithms for learning models from experience. Examples will be given of applications of these models in various areas of artificial intelligence.

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CSC412 - Uncertainty and Learning in Artificial Intelligence ||